S. Savazzi, M. Nicoli, M. Bennis, S. Kianoush and L. Barbieri, "Opportunities of Federated Learning in Connected, Cooperative, and Automated Industrial Systems," in IEEE Communications Magazine, vol. 59, no. 2, pp. 16-21, February 2021, doi: 10.1109/MCOM.001.2000200
Opportunities of federated learning in connected, cooperative, and automated industrial systems
|Author:||Savazzi, Stefano1; Nicoli, Monica2; Bennis, Mehdi3;|
1Institute of Electronics, Computer and Telecommunication Engineering (IEIIT) of Consiglio Nazionale delle Ricerche (CNR), Milano, Italy
2Politecnico di Milano DIG and DEIB department, Milano, Italy
3Centre for Wireless Communications, University of Oulu, Finland
|Online Access:||PDF Full Text (PDF, 1.3 MB)|
|Persistent link:|| http://urn.fi/urn:nbn:fi-fe2021051930604
Institute of Electrical and Electronics Engineers,
|Publish Date:|| 2021-05-19
Next-generation autonomous and networked industrial systems (i.e., robots, vehicles, drones) have driven advances in ultra-reliable low-laten-cy communications (URLLC) and computing. These networked multi-agent systems require fast, communication-efficient, and distributed machine learning (ML) to provide mission-crit-ical control functionalities. Distributed ML techniques, including federated learning (FL), represent a mushrooming multidisciplinary research area weaving together sensing, communication, and learning. FL enables continual model training in distributed wireless systems: rather than fusing raw data samples at a centralized server, FL leverages a cooperative fusion approach where networked agents, connected via URLLC, act as distributed learners that periodically exchange their locally trained model parameters. This article explores emerging opportunities of FL for the next-generation networked industrial systems. Open problems are discussed, focusing on cooperative driving in connected automated vehicles and collaborative robotics in smart manufacturing.
IEEE communications magazine
|Pages:||16 - 21|
|Type of Publication:||
A1 Journal article – refereed
|Field of Science:||
213 Electronic, automation and communications engineering, electronics
The work is partially supported by the European Project CHISTERA RadioSense (Big Data and process modelling for the Smart Industry - BDSI) funded by MUR and by the Project BASE5G (Broadband InterfAces and services for Smart Environments enabled by 5G technologies), funded by the Italian Lombardy Regional Government under the grant POR-FESR 2014-2020 ID 1155850.
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